Hi everyone. I implemented the positional encoding class just like in the pytorch tutorial:

class PositionalEncoding(nn.Module):

```
def __init__(self, d_model, max_len):
super().__init__()
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x:
x = x + self.pe[:x.size(0)]
return self.dropout(x)
```

Then I run this:

```
rand = torch.randint(10, (BATCH_SIZE, 20))
emb = PositionalEncoding(EMBED_LEN, MAX_SEQ_LEN)
out = emb(rand)
print(out.shape)
```

I expect positional encoding output here. What I end up with is:

->The size of tensor a (20) must match the size of tensor b (768) at non-singleton dimension 2

As with the paper don’t I need to feed input directly to the positional encoder? Is this error expected? What’s the best way to test if this function operates as expected?